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Campaign Analytics

alirezarezvani/claude-skills

  • 744 installs
  • 23.5k repo stars
  • Updated July 17, 2026
  • alirezarezvani/claude-skills

campaign-analytics is a Claude Code skill that generates structured A/B test plans, documents hypotheses, tracks experiment parameters, and analyzes statistical lift for developers and growth engineers running product ex

About

campaign-analytics is a skill from alirezarezvani/claude-skills that templates end-to-end A/B test analysis documentation. The readme provides sections for test name, test ID, date range, status, hypothesis (if/then/because), test design parameters, control and variant definitions, primary and secondary metrics, and traffic split. Developers reach for campaign-analytics when planning experiments, recording running tests, or summarizing completed runs with clear lift calculations instead of ad hoc spreadsheets. The skill fits growth and product teams who need reproducible experiment records tied to measurable outcomes.

  • Structured A/B test template with hypothesis, design, targeting and results sections
  • Statistical significance calculator including minimum sample size and minimum detectable effect
  • Primary and secondary metric tracking with automatic lift vs control calculations
  • Audience, channel, device and geography targeting documentation
  • Status tracking for Planning, Running, Complete and Inconclusive tests

Campaign Analytics by the numbers

  • 744 all-time installs (skills.sh)
  • Ranked #392 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Jul 31, 2026 (Skillselion catalog sync)
npx skills add https://github.com/alirezarezvani/claude-skills --skill campaign-analytics

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Listed on Skillselion
Installs744
repo stars★ 23.5k
Security audit1 / 3 scanners passed
Last updatedJuly 17, 2026
Repositoryalirezarezvani/claude-skills ↗

How do you document and analyze A/B test results?

Generate structured A/B test plans, document hypotheses, track experiment parameters, and analyze statistical results with clear lift calculations.

Who is it for?

Growth and product engineers documenting experiments with hypotheses, control/variant design, and lift calculations.

Skip if: Teams needing live experiment SDK wiring or warehouse SQL pipelines instead of structured test documentation and analysis.

When should I use this skill?

A developer needs to plan an A/B test, document hypotheses and metrics, or analyze experiment lift and statistical results.

What you get

Structured A/B test plan, hypothesis doc, experiment parameter table, and statistical lift analysis summary.

  • A/B test plan document
  • Hypothesis and metrics specification
  • Lift analysis summary

Files

SKILL.mdMarkdownGitHub ↗

Campaign Analytics

Production-grade campaign performance analysis with multi-touch attribution modeling, funnel conversion analysis, and ROI calculation. Three Python CLI tools provide deterministic, repeatable analytics using standard library only -- no external dependencies, no API calls, no ML models.

---

Input Requirements

All scripts accept a JSON file as positional input argument. See assets/sample_campaign_data.json for complete examples.

Attribution Analyzer

{
  "journeys": [
    {
      "journey_id": "j1",
      "touchpoints": [
        {"channel": "organic_search", "timestamp": "2025-10-01T10:00:00", "interaction": "click"},
        {"channel": "email", "timestamp": "2025-10-05T14:30:00", "interaction": "open"},
        {"channel": "paid_search", "timestamp": "2025-10-08T09:15:00", "interaction": "click"}
      ],
      "converted": true,
      "revenue": 500.00
    }
  ]
}

Funnel Analyzer

{
  "funnel": {
    "stages": ["Awareness", "Interest", "Consideration", "Intent", "Purchase"],
    "counts": [10000, 5200, 2800, 1400, 420]
  }
}

Campaign ROI Calculator

{
  "campaigns": [
    {
      "name": "Spring Email Campaign",
      "channel": "email",
      "spend": 5000.00,
      "revenue": 25000.00,
      "impressions": 50000,
      "clicks": 2500,
      "leads": 300,
      "customers": 45
    }
  ]
}

Input Validation

Before running scripts, verify your JSON is valid and matches the expected schema. Common errors:

  • Missing required keys (e.g., journeys, funnel.stages, campaigns) → script exits with a descriptive KeyError
  • Mismatched array lengths in funnel data (stages and counts must be the same length) → raises ValueError
  • Non-numeric monetary values in ROI data → raises TypeError

Use python -m json.tool your_file.json to validate JSON syntax before passing it to any script.

---

Output Formats

All scripts support two output formats via the --format flag:

  • --format text (default): Human-readable tables and summaries for review
  • --format json: Machine-readable JSON for integrations and pipelines

---

Typical Analysis Workflow

For a complete campaign review, run the three scripts in sequence:

# Step 1 — Attribution: understand which channels drive conversions
python scripts/attribution_analyzer.py campaign_data.json --model time-decay

# Step 2 — Funnel: identify where prospects drop off on the path to conversion
python scripts/funnel_analyzer.py funnel_data.json

# Step 3 — ROI: calculate profitability and benchmark against industry standards
python scripts/campaign_roi_calculator.py campaign_data.json

Use attribution results to identify top-performing channels, then focus funnel analysis on those channels' segments, and finally validate ROI metrics to prioritize budget reallocation.

---

How to Use

Attribution Analysis

# Run all 5 attribution models
python scripts/attribution_analyzer.py campaign_data.json

# Run a specific model
python scripts/attribution_analyzer.py campaign_data.json --model time-decay

# JSON output for pipeline integration
python scripts/attribution_analyzer.py campaign_data.json --format json

# Custom time-decay half-life (default: 7 days)
python scripts/attribution_analyzer.py campaign_data.json --model time-decay --half-life 14

Funnel Analysis

# Basic funnel analysis
python scripts/funnel_analyzer.py funnel_data.json

# JSON output
python scripts/funnel_analyzer.py funnel_data.json --format json

Campaign ROI Calculation

# Calculate ROI metrics for all campaigns
python scripts/campaign_roi_calculator.py campaign_data.json

# JSON output
python scripts/campaign_roi_calculator.py campaign_data.json --format json

---

Scripts

1. attribution_analyzer.py

Implements five industry-standard attribution models to allocate conversion credit across marketing channels:

ModelDescriptionBest For
First-Touch100% credit to first interactionBrand awareness campaigns
Last-Touch100% credit to last interactionDirect response campaigns
LinearEqual credit to all touchpointsBalanced multi-channel evaluation
Time-DecayMore credit to recent touchpointsShort sales cycles
Position-Based40/20/40 split (first/middle/last)Full-funnel marketing

2. funnel_analyzer.py

Analyzes conversion funnels to identify bottlenecks and optimization opportunities:

  • Stage-to-stage conversion rates and drop-off percentages
  • Automatic bottleneck identification (largest absolute and relative drops)
  • Overall funnel conversion rate
  • Segment comparison when multiple segments are provided

3. campaign_roi_calculator.py

Calculates comprehensive ROI metrics with industry benchmarking:

  • ROI: Return on investment percentage
  • ROAS: Return on ad spend ratio
  • CPA: Cost per acquisition
  • CPL: Cost per lead
  • CAC: Customer acquisition cost
  • CTR: Click-through rate
  • CVR: Conversion rate (leads to customers)
  • Flags underperforming campaigns against industry benchmarks

---

Reference Guides

GuideLocationPurpose
Attribution Models Guidereferences/attribution-models-guide.mdDeep dive into 5 models with formulas, pros/cons, selection criteria
Campaign Metrics Benchmarksreferences/campaign-metrics-benchmarks.mdIndustry benchmarks by channel and vertical for CTR, CPC, CPM, CPA, ROAS
Funnel Optimization Frameworkreferences/funnel-optimization-framework.mdStage-by-stage optimization strategies, common bottlenecks, best practices

---

Best Practices

1. Use multiple attribution models -- Compare at least 3 models to triangulate channel value; no single model tells the full story. 2. Set appropriate lookback windows -- Match your time-decay half-life to your average sales cycle length. 3. Segment your funnels -- Compare segments (channel, cohort, geography) to identify performance drivers. 4. Benchmark against your own history first -- Industry benchmarks provide context, but historical data is the most relevant comparison. 5. Run ROI analysis at regular intervals -- Weekly for active campaigns, monthly for strategic review. 6. Include all costs -- Factor in creative, tooling, and labor costs alongside media spend for accurate ROI. 7. Document A/B tests rigorously -- Use the provided template to ensure statistical validity and clear decision criteria.

---

Limitations

  • No statistical significance testing -- Scripts provide descriptive metrics only; p-value calculations require external tools.
  • Standard library only -- No advanced statistical libraries. Suitable for most campaign sizes but not optimized for datasets exceeding 100K journeys.
  • Offline analysis -- Scripts analyze static JSON snapshots; no real-time data connections or API integrations.
  • Single-currency -- All monetary values assumed to be in the same currency; no currency conversion support.
  • Simplified time-decay -- Exponential decay based on configurable half-life; does not account for weekday/weekend or seasonal patterns.
  • No cross-device tracking -- Attribution operates on provided journey data as-is; cross-device identity resolution must be handled upstream.

Related Skills

  • analytics-tracking: For setting up tracking. NOT for analyzing data (that's this skill).
  • ab-test-setup: For designing experiments to test what analytics reveals.
  • marketing-ops: For routing insights to the right execution skill.
  • paid-ads: For optimizing ad spend based on analytics findings.

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FAQ

What does campaign-analytics document for an A/B test?

campaign-analytics documents test name, ID, dates, status, hypothesis, control and variant definitions, primary and secondary metrics, and traffic split. The template supports planning, running, and completed experiment states.

Can campaign-analytics help analyze experiment results?

campaign-analytics helps analyze statistical results with clear lift calculations after a test completes. The skill structures experiment parameters so growth engineers can compare variants against defined primary metrics.

Is Campaign Analytics safe to install?

skills.sh reports 1 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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